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Finding higher order motifs under the levenshtein measure

Finding higher order motifs under the levenshtein measure

引用

ieee conference on bioinformatics

作者： E.F. Adebiyi T. Dipe Department of Mathematics and Computer Science University of Ilorin Ilorin Nigeria

We study the problem of finding higher order motifs under the levenshtein measure, otherwise known as the edit distance. In the problem set-up, we are given N sequences, each of average length n, over a finite alphabet /spl Sigma/ and thresholds D and q, we are to find composite motifs that contain motifs of length P (these motifs occur with almost D differences) in 1 /spl les/ q /spl les/ N distinct sequences. Two interesting but involved algorithms for finding higher order motifs under the edit distance was presented by Marsan and Sagot. Their second algorithm is much more complicated and its complexity is asymptotically not better. Their first algorithm runs in O(M /spl middot/ N/sup 2/n/sup 1+/spl alpha/ /spl middot/p /spl middot/ pow(/spl epsi/)/) where p /spl ges/ 2, /spl alpha/ > 0, pow(/spl epsi/) is a concave function that is less than 1, /spl epsi/= D/P and M is the expected number of all monad motifs. We present an alternative algorithmic approach also for Edit distance based on the concept described. The resulting algorithm is simpler and runs in O(N/sup 2/n/sup 1+p /spl middot/ pow(/spl epsi/)/) expected time.

关键词： Mathematics Computer science DNA computational biology Biological processes Organisms Measurement units Hamming distance Random sequences bioinformatics

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Using rule induction methods to analyze gene expression data

Using rule induction methods to analyze gene expression data

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ieee conference on bioinformatics

作者： G. Livingston X. Li G. Li L. Hao J. Zhou University of Massachusetts Lowell USA

We have applied rule induction to a publicly available adenocarcinoma gene expression data set. The typical approach to the analysis of gene expression data is to cluster the genes. However, interpreting the resulting clusters may be difficult. With rules, the interpretation is more obvious (e.g., (CDKN3>253)/spl rarr/(tumor-stage=3)). Our rule induction tool is a new semiautonomous discovery system we are developing called HAMB, and we used it to learn rules for survival status, survival time, and tumor stage. When we searched the world-wide web for publications relating our top 53 genes from our discovered rules to lung cancer, we found that 9 of them are known to be associated with lung cancer, 19 of them are known to be associated with other types of cancer, and the remaining 25 were not known to be associated with cancer. Our results suggest that the latter two groups of genes should be examined more closely for their association with lung cancer.

关键词： Data analysis Gene expression Cancer Data mining Filtering Induction generators Lung neoplasms Genetic mutations Demography bioinformatics

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Preliminary wavelet analysis of genomic sequences

Preliminary wavelet analysis of genomic sequences

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ieee conference on bioinformatics

作者： J. Ning C.N. Moore J.C. Nelson Delaware Biotechnology Institute University of Delaware Newark DE USA Department of Mathematics Kansas State University Manhattan KS USA Department of Plant Pathology Kansas State University Manhattan KS USA

Large genome-sequencing projects have made urgent the development of accurate methods for annotation of DNA sequences. Existing methods combine ab inito pattern searches with knowledge gathered from comparison with sequence databases or from training sets of known genes. However, the accuracy of these methods is still far from satisfactory. In the present study, wavelet algorithms in combination with entropy method are being developed as an alternative way to determine gene locations in genomic DNA sequences. Wavelet methods seek periodicity present in sequences. A promising advantage of wavelets is their adaptivity to varying lengths of coding/noncoding regions. Moreover, the wavelet methods integrated with entropy method just search the information contents of the sequences, which do not need to be trained. The preliminary results show that the wavelet approach is feasible and may be better than some knowledge-dependent approaches based on a sample of genomic DNA sequences.

关键词： Wavelet analysis Genomics bioinformatics Sequences DNA Hidden Markov models Entropy Signal processing algorithms Charge carrier processes Concatenated codes

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Spectral decomposition of the Laplacian matrix applied to RNA folding prediction

Spectral decomposition of the Laplacian matrix applied to RN...

引用

ieee conference on bioinformatics

作者： D. Barash Genome Diversity Center Institute of Evolution University of Haifa Haifa Israel

RNA secondary structure consists of elements such as stems, bulges, loops. The most obvious and important scalar number that can be attached to an RNA structure is its free energy, with a landscape that governs the folding pathway. However, because of the unique geometry of RNA secondary structure, another interesting single-signed scalar number based on geometrical scales exists that can assist in RNA structure computations. This scalar number is the second eigenvalue of the Laplacian matrix corresponding to a tree-graph representation of the RNA secondary structure. Because of the mathematical properties of the Laplacian matrix, the first eigenvalue is always zero, and the second eigenvalue (often denoted as the Fiedler eigenvalue) is a measure of the compactness of the associated tree-graph. The concept of using the Fiedler eigenvalue/eigenvector is borrowed from domain decomposition in parallel computing. Thus, along with the free energy, the Fiedler eigenvalue can be used as a signature in a clever search among a collection of structures by providing a similarity measure between RNA secondary structures. This can also be used for mutation predictions, classification of RNA secondary folds, filtering and clustering. Furthermore, the Fiedler eigenvector may be used to chop large RNAs into smaller fragments by using spectral graph partitioning, based on the geometry of the secondary structure. Each fragment may then be treated differently for the folding prediction of the entire domain.

关键词： Laplace equations Matrix decomposition RNA Eigenvalues and eigenfunctions Genetic mutations bioinformatics Parallel processing Genomics computational geometry Energy measurement

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Computing highly specific and mismatch tolerant oligomers efficiently

Computing highly specific and mismatch tolerant oligomers ef...

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ieee conference on bioinformatics

作者： T. Yamada S. Morishita Department of Computational Biology Graduate School of Frontier Sciences University of Tokyo Japan

The sequencing of the genomes of a variety of species and the growing databases containing expressed sequence tags (ESTs) and complementary DNAs (cDNAs) facilitate the design of highly specific oligomers for use as genomic markers, PCR primers, or DNA oligo microarrays. The first step in evaluating the specificity of short oligomers of about twenty units in length is to determine the frequencies at which the oligomers occur. However, for oligomers longer than about fifty units this is not efficient, as they usually have a frequency of only 1. A more suitable procedure is to consider the mismatch tolerance of an oligomer, that is, the minimum number of mismatches that allows a given oligomer to match a sub-sequence other than the target sequence anywhere in the genome or the EST database. However, calculating the exact value of mismatch tolerance is computationally costly and impractical. Therefore, we studied the problem of checking whether an oligomer meets the constraint that its mismatch tolerance is no less than a given threshold. Here, we present an efficient dynamic programming algorithm solution that utilizes suffix and height arrays. We demonstrated the effectiveness of this algorithm by efficiently computing a dense list of oligo-markers applicable to the human genome. Experimental results show that the algorithm runs faster than well-known Abrahamson 's algorithm by orders of magnitude and is able to enumerate 63%/spl sim/79% of qualified oligomers.

关键词： Sequences bioinformatics Genomics Humans Databases DNA Frequency Gene expression Fluorescence Biology computing

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Degenerate primer design via clustering

Degenerate primer design via clustering

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ieee conference on bioinformatics

作者： X. Wei D.N. Kuhn Giri Narasimhan School of Computer Science Florida International University Miami FL USA Subtropical Horticulture Research Station USDA-ARS Miami FL USA Department of Biological Sciences Florida International University Miami FL USA

This paper describes a new strategy for designing degenerate primers for a given multiple alignment of amino acid sequences. Degenerate primers are useful for amplifying homologous genes. However, when a large collection of sequences is considered, no consensus region may exist in the multiple alignment, making it impossible to design a single pair of primers for the collection. In such cases, manual methods are used to find smaller groups from the input collection so that primers can be designed for individual groups. Our strategy proposes an automatic grouping of the input sequences by using clustering techniques. Conserved regions are then detected for each individual group. Conserved regions are scored using a blocksimilarity score, a novel alignment scoring scheme that is appropriate for this application. Degenerate primers are then designed by reverse translating the conserved amino acid sequences to the corresponding nucleotide sequences. Our program, DePiCt, was written in BioPerl and was tested on the Toll-Interleukin Receptor (TIR) and the nonTIR family of plant resistance genes. Existing programs for degenerate primer design were unable to find primers for these data sets.

关键词： Proteins bioinformatics Genomics Organisms Computer science US Department of Agriculture Amino acids Immune system Pathogens Biology

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SNP analysis system for detecting complex disease associated sites

SNP analysis system for detecting complex disease associated...

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ieee conference on bioinformatics

作者： Y. Higashi H. Higuchi T. Kido H. Matsumine M. Baba T. Morimoto M. Muramatsu NTT Data Company Limited Tokyo Japan HuBit Genomix Inc. Tokyo Japan School of Biomedical Science Tokyo Medical and Dental University Tokyo Japan

We developed a system that supports disease association studies to detect genes that may cause complex diseases. The main function of the system is to examine the possibility of each polymorphism being associated with a disease. Another important function is to perform linkage disequilibrium analysis and combine SNPs (single nucleotide polymorphisms) together into LDblocks (linkage-disequilibrium-blocks) to improve statistical power for association study. Those analyses can be efficiently performed using an analysis pipeline of the new system with handy tools for eliminating the inadequate data and so on. Consequently, the number of SNPs the system can analyze is about 30 to 50 times higher than by the standard manual procedures per unit of time. The new system also has a sophisticated visualization tool. The main viewer displays the genomic structure and is linked to another main viewer showing the in-depth analysis result. These viewers let the user easily check and make an interpretation of the results. The new system should provide significant assistance for the genome research of complex diseases.

关键词： Diseases Genomics bioinformatics Testing Couplings Performance analysis Proteins Bayesian methods Software algorithms Poles and towers

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What makes IgG binding domain of protein L fold up to native state: a simulation study with physical oriented energy functions coupled to topology induced terms

What makes IgG binding domain of protein L fold up to native...

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ieee conference on bioinformatics

作者： Seung Yup Lee Y. Fujitsuka S. Takada Do Hyun Kim Department of Chemical and Biomolecular Engineering and Center For Ultrochemical Process System Korea Advanced Institute of Science and Technology Daejeon South Korea Graduate School of Science and Technology Kobe University Japan PRESTO Japan Science and Technology Corporation Nada Japan

The folding pathways and mechanisms of IgG binding domain of protein L composed of 62 residues are simulated by an over-damped Langevin dynamics with a coarse-grained chain representation. Physical oriented effective energy functions (EEFs) are employed for sequence-specific interactions as well as topology induced energies to bias overall energies to native basin. We observed the preferential formation of N terminal hairpin and the break of structural symmetry during folding. In the free energy profile calculated from equilibrium sampling and histogram method, it clearly shows two state folding scenario with transition state (TS). In the TS regime, N terminal hairpin already forms whereas C terminal hairpin and alpha helix are not structured yet. The predicted results are fully consistent with experimental data. Moreover, we found that hydrophobicity and secondary local propensity among many physical interactions determine the overall folding routes significantly by reduced model studies.

关键词： Proteins Topology bioinformatics

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Discovering compact and highly discriminative features or combinations of drug activities using support vector machines

Discovering compact and highly discriminative features or co...

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ieee conference on bioinformatics

作者： H. Yu J. Yang W. Wang J. Han Department of Computer Science University of Illinois Urbana IL USA Department of Computer Science University of Illinois Urbana-Champaign IL USA Department of Computer Science University of North Carolina Chapel Hill NC USA

Nowadays, high throughput experimental techniques make it feasible to examine and collect massive data at the molecular level. These data, typically mapped to a very high dimensional feature space, carry rich information about functionalities of certain chemical or biological entities and can be used to infer valuable knowledge for the purposes of classification and prediction. Typically, a small number of features or feature combinations may play determinant roles in functional discrimination. The identification of such features or feature combinations is of great importance. In this paper, we study the problem of discovering compact and highly discriminative features or feature combinations from a rich feature collection. We employ the support vector machine as the classification means and aim at finding compact feature combinations. Comparing to previous methods on feature selection, which identify features solely based on their individual roles in the classification, our method is able to identify minimal feature combinations that ultimately have determinant roles in a systematic fashion. Experimental study on drug activity data shows that our method can discover descriptors that are not necessarily significant individually but are most significant collectively.

关键词： Drugs Support vector machines bioinformatics

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The GeneCards/spl trade/ family of databases: GeneCards, GeneLoc, GeneNote and GeneAnnot

The GeneCards/spl trade/ family of databases: GeneCards, Gen...

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ieee conference on bioinformatics

作者： M. Safran V. Chalifa-Casp O. Shmueli N. Rosen H. Benjamin-Rodrig R. Ophir I. Yanai M. Shmoish D. Lancet Biological Services (Bioinformatics and Biological Computing Unit) The Weizmann Institute of Science Rehovot Israel Biol. Services (Bioinf. & Biol. Comput. Unit) Weizmann Inst. of Sci. Rehovot Israel Department. of Molecular Genetics Weizmann Institute of Science Rehovot Israel Depts of 'Molecular Genetics

The popular GeneCards/spl trade/ integrated database of human genes, genomic maps, proteins and diseases has recently spawned three related functional genomics efforts. As sequence data rapidly accumulates, the bottleneck in biology shifts from data production to analysis; researchers seek a profound understanding of the role of each gene, and of the way genes function together. GeneLoc integrates human gene collections by comparing genomic coordinates at the exon level, eliminating redundancies, and assigning unique and meaningful location-based identifiers. GeneCards expression tissue vectors are provided by GeneNote, the first effort to present sophisticated expression analyses for a variety of normal human tissues using the full complement of gene representations (Affymetrix arrays HG-U95A-E). The GeneAnnot system aligns probe-sets with the major public repositories of human mRNA sequences, and provides detailed annotation for each probe-set, with links to GeneCards.

关键词： Databases Humans Genomics bioinformatics Sequences Proteins Diseases Production Data analysis Genetic expression

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